The effects of response inhibition training following binge memory retrieval in young adults binge eaters: a randomised-controlled experimental study

Binge eating is increasingly prevalent among adolescents and young adults and can have a lasting harmful impact on mental and physical health. Mechanistic insights suggest that aberrant reward-learning and biased cognitive processing may be involved in the aetiology of binge eating. We therefore investigated whether recently developed approaches to catalyse brief interventions by putatively updating maladaptive memory could also boost the effects of cognitive bias modification training on binge eating behaviour. A non-treatment-seeking sample of 90 binge eating young adults were evenly randomised to undergo either selective food response inhibition training, or sham training following binge memory reactivation. A third group received training without binge memory reactivation. Laboratory measures of reactivity and biased responses to food cues were assessed pre-post intervention and bingeing behaviour and disordered eating assessed up to 9 months post-intervention. The protocol was pre-registered at https://osf.io/82c4r/. We found limited evidence of premorbid biased processing in lab-assessed measures of cognitive biases to self-selected images of typical binge foods. Accordingly, there was little evidence of CBM reducing these biases and this was not boosted by prior ‘reactivation’ of binge food reward memories. No group differences were observed on long-term bingeing behaviour, caloric consumption or disordered eating symptomatology. These findings align with recent studies showing limited impact of selective inhibition training on binge eating and do not permit conclusions regarding the utility of retrieval-dependent memory ‘update’ mechanisms as a treatment catalyst for response inhibition training.


Scientific Reports
| (2022) 12:9281 | https://doi.org/10.1038/s41598-022-12173-w www.nature.com/scientificreports/ measured differences in recent depression with the Beck Depression Inventory (BDI 59 ). Impulsivity; a putative predictor of binge behaviour, was indexed using the Barratt Impulsiveness Scale (BIS 60 ). A calendar-based self-report Timeline-Follow-Back measure was used to measure subjective binge frequency 61 , where participants reported the incidence of subjective binges; defined as 'eating an unusually large amount of food with the subjective feeling of loss-of-control'. This was confirmed by completion of a daily food diary via the MyFitnessPal app. Participants were asked to log everything they consumed for one week prior to session 1 (baseline), from session 2 to session 3 (post-intervention) and post-session-3 (follow-up). From this, total daily calories, carbohydrates, fats and sugars were calculated.
HPF Cue reactivity and 'taste test'. The procedure is outlined in detail in the Supplementary Information. Briefly, 'pleasantness' , 'desire to eat' and 'likelihood of bingeing on' was assessed for 18 HPF and 18 LPF images on a 0-100 scale. From this task, individualised HPF and LPF images (four of each) were selected per-participant, for later use in the visual probe and Go/No-Go tasks based on highest and lowest reward reactivity ratings. Prior to image rating, participants selected a preferred HPF snack food item from a 'menu' and were told they would eat this after rating some food images, in a sham 'taste test' . The selected food was placed in front of the participant and visible during the ratings of all food images and at the end the picture rating, was itself rated for 'desire to eat' and predicted 'enjoyment' pre-consumption and its taste attributes, true 'enjoyment' and 'wanting more' , post-consumption. The food was consumed according to on screen prompts requiring participants to 'pick up food' , 'prepare to eat' and 'eat the food' .
Go/No-Go Task. Response bias to binge foods was both assessed and retrained via a Go/No-Go task, adapted from Houben and Jansen 42 and following previous research 38,62 . Full task details are given in the Supplementary Information and Ref. 63 . An 'assessment version' of the task was used in Sessions 1 and 3 and a 'modification version' on Session 2 ('intervention' session). Task parameters were identical in both versions except HPF binge foods were paired with 'No-go' responses and LPF images paired with 'Go' responses on 100% trials in the 'modification' version. The 'sham' version of the Go/No-Go task on session 2 was simply the 'assessment' version; with parity between requirement for Go-or No-go responses for all stimulus types (HPF binge food, LPF or filler). Assessed indices of response bias were error rates, median reaction times, sensitivity (d-prime) and response bias (criterion C), indexing bias to 'go' to images regardless of response requirement 42 .
Visual probe. Eye-tracking in a dot-probe task was used to assess attentional bias to the self-selected LPF and HPF stimuli. All food images were paired with matched non-food images and dwell time and first fixation latency were calculated as indices of sustained and automatic attention, respectively. Details in Supplementary Information.
Binge memory retrieval and no-retrieval control. Participants in the BMR + RIT and BMR + sham groups underwent Binge Memory Retrieval (BMR) which followed a procedure parallel to those we have used successfully in previous studies on maladaptive reward memory reconsolidation 48,64 . The BMR procedure was introduced to the participants as a repeat of the session one 'taste test' (i.e. cue reactivity) task. Again, participants selected their favourite food from the 'menu' and were instructed that they would consume this after rating images. The presented images were the participant's four highest-rated 'binge cues' . They then rated their predicted enjoyment and 'desire to eat' their selected food. Following this, the on-screen consumption prompts read as before. The final prompt, however, read 'Stop, put food down' at which point the food was taken away. Participants were thus prevented from consuming their anticipated food reward, putatively engendering a cognitive prediction error.
Participants in the NR condition followed the same procedure as BMR, except: (1) the binge food cues were replaced with the lowest-rated LPF food images from the cue reactivity task (2) Instead of selecting their favourite HPF from the menu, participants were given a non-binge LPF (celery sticks) and told they would eat this after rating food images. Thereafter, the image and food ratings and prompt screens were identical to the BMR procedure, including the prediction error procedure. The NR procedure was designed to match the BMR as closely as possible without (re)activating binge food reward memory.
Procedure. After screening, participants attended three lab sessions and (remotely) provided follow-up data on four additional occasions (+ 2 week, 3 months, 6 months, 9 months). Prior to lab sessions they fasted from solid food (4 h) and abstained from caffeine (2 h). All lab sessions were conducted between 1 and 5 p.m. Written informed consent was given at the start of Session 1, following eligibility screening. The full procedure is outlined in detail in the Supplementary Information. Session 1. Baseline demographic, questionnaire, biological (including blood glucose, blood pressure, weight & height for BMI calculation) and eating-related measures were obtained (see supplement for full list). In addition, state measures of food craving (FCQ) and hunger (hunger ruler) were assessed followed by the cue reactivity procedure and the assessment version of the Go/No-Go task. Finally, they completed the visual probe task.

Session 2 (session 1 + 48 h).
After repeating the biological and state measures from session 1, participants then completed the BMR or NR procedure as appropriate to their random group allocation. As with our previous studies 48,49 , following the BMR or NR procedure, participants completed high-load working memory tasks (prose recall from the Rivermead battery and digit span forwards and backwards), to ensure cognitive disen-Ethical approval. The authors assert that all procedures contributing to this work were approved by and comply with University College London Research Ethics Committee's ethical standards on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. ISRCTN Registration Identifier: ISRCTN13262256. Open Science Framework Pre-registration: https:// osf. io/ 82c4r/.

Results
Descriptive statistics for key variables across groups are given in Table 1. Groups were very similar on assessed demographic variables, being typically in their early 20 s and in higher education. BES scores verified subjective binge-eating status and the PFS, TFEQ and FCQ indicated relatively high reactivity to food, emotional/uncontrolled eating and food craving indicating the sample displayed robust maladaptive reward responses to food. There was a trend for greater BMI in BMR + RIT than the other groups, due to three individuals with particularly high BMI (~ 37). There was also a trend for a difference (BMR + Sham > BMR + RIT) in the uncontrolled eating subscale of the TFEQ. Neither of these differences approached significance at FDR-corrected alpha. Groups were otherwise similar on baseline variables.

Short-term effects of RIT and BMR (in-lab measures).
Pre-manipulation, Go/No-Go task commission errors (False Alarms) were greater for both types of food stimuli (LPF and binge) than non-food stimuli. See Supplementary Information for full analyses. Error rates were examined across Group, Session (pre-manipulation vs post-manipulation), Stimulus Types (Binge, LPF, non-food filler) and Error Type (misses and false alarms). In line with analysis of baseline data, main effects of Stimulus Type (χ 2 (2) = 82.194, p < 0.001), Error Type (false alarms > misses): χ 2 (1) = 6.404, p = 0.011 and their interaction (χ 2 (2) = 13.013, p = 0.001) were found. The four-way interaction of Group, Stimulus Type, Error Type and Session was also significant. A three-way Stimulus Type × Session × Error Type interaction was present in all groups, although simple effects within each Group showed a change in response to Binge food stimuli only in BMR + RIT (see Table 2, top). At baseline, BMR + RIT showed significantly more false alarms than misses to binge food images (χ 2 (1) = 18.043, p < 0.001), however this was abolished post-training (χ 2 (1) = 1.222, p = 0.269).
To qualify this effect, Session × Stimulus Type interactions were assessed within each Group and Error Type (see Table 2, bottom). This showed a significant increase in binge-food 'misses' from session 1 to session 3 in BMR + RIT, but a significant decrease in misses (i.e. greater response to binge food) in BMR + Sham, indicating potential worsening of approach bias in this group. In NR + RIT, there was a significant decrease in false alarms on binge food 'no-go' trials and a decrease in false alarms to filler images. In BMR + Sham, there was a significant worsening of response bias to food, reflected in a reduction in C for binge images from session 1 to session 3 [F (1,90) = 6.14,p = 0.015]. In BMR + RIT, there was a significant reduction in bias to binge images (increase in C towards 0) [F (  Long-term disorder-relevant outcomes. Binge Eating Scale. The mixed modelling approach for the BES was supported by significant variance in intercepts (σ 2 = 37.315, Z = 5.519, p < 0.001). A significant effect of Time (baseline (session 1), post-manipulation (session 3), 2 weeks, 3 months, 6 months, 9 months) was observed, indicating reduction in symptom severity across the study, but no effects of Group nor interaction (see Table 3). Despite significant variance in the , similarly provided evidence in favour of no differences post-intervention, but the latter were uninformative at subsequent time-points owing to reduced sample size. BF 01 s for these contrasts are given in Table 3.   (10,85) = 0.862, p = 0.572]. The same pattern of results was found when modelling mean daily binge calories (using a gamma GLMM with log link). The intervention thus had no differential impact on bingeing behaviour (Table 4). Bayes factors favoured the null in one-way ANOVA at each time point and favoured no difference, or were inconclusive, for t-tests between RIT and sham (see Table 4).

Discussion
Learned cognitive biases have been posited to be an important factor in maintaining binge eating behaviour 66 and a prime target for therapeutic intervention. This study sought to examine the possible augmentation of therapeutic efficacy of food response inhibition training (RIT) via putative 'reconsolidation-update' mechanisms in sub-clinical binge eating young adults. Participants generally showed robust reductions across the spectrum of maladaptive binge behaviours assessed. However, we found very little evidence for beneficial effects of RIT on either short-term indices of response biases (Go/No-Go and visual probe and cue reactivity), nor any clinically-relevant measures of eating disorder symptomatology (binge episodes, BES, YFAS). Equally, a retrieval procedure designed to elicit memory destabilisation prior to bias retraining produced minimal augmentation of subsequent RIT effects. Despite evident bingeing behaviour and cognitive symptomatology, our sample displayed extremely high performance accuracy on the Go/No-Go task, indicating relatively little in the way of premorbid response inhibition deficits to binge cues and possibly restricting the potential impact RIT a priori . Via signal detection analysis, we observed significant, albeit modest, 'go' biases to all food stimuli (both HPF/binge foods and LPF/ non-binge foods) and automatic visual attentional capture by HPFs in eye-tracking metrics. We found greater reductions in response bias on the Go/No-Go task in BMR + CBM, but insufficient evidence that these differences were substantively related to eating behaviour.
While promising short-to-medium-term effects of food inhibitory control training have previously been seen in laboratory studies in 'healthy volunteers' 43,67,68 and 'obese' individuals (primary researchers' own terms) 69 , both this and recent research has observed modest effects in the majority of tested longer-term clinical endpoints and eating behaviour in disordered eating groups 46,[70][71][72][73] . These inconsistencies may be due to different effects across (dis)ordered eating populations, focus on short-term (lab-based) vs. lasting effects and training parameters and control procedures, which have been shown to be key determinates of food response inhibition train effects 45 .
We adapted the Go/No-Go task which effectively reduced chocolate 'go' bias and consumption in previous research 42 . It is possible that the greater diversity of high-palatability food (HPF) stimuli used here were less evocative of response biases than chocolate-only stimuli, producing more heterogeneous responses. However, the HPF stimuli in our study were individualised based on idiosyncratic ratings of the most rewarding images from a pool of food images with high normative ratings for reward value 74 . Reactivity to these images should therefore be as high as could be expected within the bounds of an experimental setting. Eye-tracking data confirmed that HPF stimuli were salient, robustly inducing automatic visual orienting 75 to a greater extent than low palatability foods, an index predictive of actual food intake 76 .
A more compelling explanation for the disparate findings is the inflation of previous studies' effects by suboptimal choice of (or lack of) control for inhibitory training procedures 34 . Earlier studies on RIT in chocolate consumption employed a 'control' condition that pairs chocolate images with 'go' responses. This 'go control' is not inert, in that it may increase approach bias to chocolate images and maximise the apparent effect of RIT by artificially inflating the difference between conditions. Indeed, studies using such 'opposing control' conditions tend to show significant effects, whereas those using true 'sham' training, (50/50 Go/No-Go) do not 78,79 but see Ref, an effect verified experimentally by Adams et al. 45 . As 'food → go' training may worsen overeating symptoms, Table 4. Changes in mean binge episode frequency from baseline to all post-intervention time points. T-tests are vs. baseline. Bayes Factors are for between-groups comparisons at post-intervention time points (ANOVA or Mann-Whitney U). Null hypothesis = (no group difference) vs alternative (group means unequal for ANOVA; RIT bingeing < Sham bingeing in Mann-Whitney). Bayes factors > 3 are considered evidence in favour of the null. Most studies on RIT employ immediate or 24-h post tests and it is possible that the 1-week delay between training and test we used prevented us from observing any immediate effects of training. However, we have observed effects in harmful drinkers from a similarly brief, single post-retrieval interventions that have been evident at 1-week and at least 9-months afterwards. If RIT effects are only observable in laboratory measures and at short latencies, we must question the comparative clinical utility of such an approach.
Clearly, null findings with a specific form of CBM (RIT) here do not preclude possible effects of other CBM modalities, such as approach bias modification, which, at least within the domain of AUD, have shown more consistent clinical effects. It is possible that alternative forms of CBM would have been more effective than the RIT used in the current study. The relative efficacy of these different modalities in changing maladaptive eating behaviour remains an open question in need of assessment. However, an examination of experimental evidence published since pre-registering this study and collecting the data (https:// osf. io/ hjtw3) indicates that this pattern of inconsistent findings is not specific to RIT, but is reflected in the broader literature examining CBM in binge and over-eating 46,71,80,81 , calling us to question the key moderators and potential therapeutic impact of cognitive bias modification. More robust effects of CBM generally, have been found for alcohol use disorders 82 , suggesting effects may be reward-domain specific. Indeed, although the authors of a recent narrative review concluded favourably for CBM across reward domains 78 , evidence for its efficacy in modifying eating behaviour has been questioned by authors of primary studies 79 who note inconsistent findings and inappropriate CBM control groups. As a whole, therefore, the field would benefit from more consistent and well-controlled task design, larger randomised controlled studies with longer-term follow-up and direct assessment of the relative efficacy of different CBM modalities across different reward domains.
Limitations. The aim of this study was to assess whether RIT efficacy could be catalysed by conducting retraining following 'reactivation' of maladaptive food reward memory, as we have shown for behavioural and drug interventions 48,50 . We did not find evidence of such effects, aside from in short-lived Go/No-Go task performance interpreting this as a reconsolidation-update effect would be tenuous. However, demonstrating therapeutic enhancement via maladaptive memory reminders is dependent upon a memory-targeting intervention having a minimum of standalone efficacy. Since CBM was largely ineffective, even in the short term (in-lab) measures of responding collected here, we are unable to make any conclusions as to whether food reward memories were successfully destabilised our retrieval procedure nor whether this could confer additive benefit in longer-term clinical outcomes to a standalone behavioural therapy for binge eating. Multiple sessions of training could be used, although one of the great appeals of a putative reconsolidation-based therapy is its single-shot nature.
Binge-eating individuals frequently already engage in compensatory strategies to regulate their weight and minimise binge episodes, including effortful inhibition of food approach, and avoidance of 'trigger' foods. Our eye tracking data support this notion. The complex relationship that binge eating individuals have with binge foods thus entails reward and approach, but also avoidance, self-criticism and shame 84 following bingeing. If binge-eating individuals are already well-practiced in trigger food avoidance strategies, the potential for added efficacy of brief avoidance training may have been limited a priori. Identifying target sub-groups with high levels of baseline response bias may yield greater effects of retraining. While we have found minimal evidence in the current study to recommend RIT as a clinical intervention in binge eating, given the relative ease of its implementation (e.g. via smartphone apps), limited potential for harm when constructed correctly and potential to orient more attention to one's eating behaviours and related cognitions, there may be a rationale to recommend pursuing RIT approaches in these groups.
Our study sample were not receiving treatment and binge eating behaviour was primarily assessed via selfreport instruments, which may be considered sub-optimal. However, it was not our intention to diagnose binge eating in this study and we focussed upon adolescent sub-clinical binge eaters as a group in whom preventative, low intensity interventions might be usefully employed. The existence of the relevant disordered eating behaviours was further triangulated against other disordered eating measures and food logs. Regarding these, one reviewer noted that MyFitnessPal usage is prevalent among disordered eating populations 85 and on pro-eating disorder forums, questioning the ethics of it use in the current setting. While there is no current evidence for a causal link between MyFitnessPal usage and eating disorders and the reductions in eating disorder symptomatology across all groups in the current study suggest it was not a cause of harm, future studies may instead wish to use recovery-focussed apps, such as Recovery Record.
Strengths. We employed a highly rigorous randomised, pre-registered design including more appropriate control procedures than some previous studies, and comprehensive assessment of both short-term target cognitive processes and long-term eating behaviour and disorder symptomatology, with a follow-up period considerably longer than prior research. This allows us to fairly comprehensively reject the possibility of lasting intervention efficacy over a clinically-relevant timeframe. Doing so, we found no evidence for a lasting beneficial effect of inhibitory control training, either alone or when combined with pre-training maladaptive memory retrieval.

Data availability
Study data are available upon request from Ravi Das.